





"""simple docstring"""





# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


SCREAMING_SNAKE_CASE__   =  Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

SCREAMING_SNAKE_CASE__   =  {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

SCREAMING_SNAKE_CASE__   =  "zero2"
SCREAMING_SNAKE_CASE__   =  "zero3"
SCREAMING_SNAKE_CASE__   =  [ZEROa, ZEROa]


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Tuple				,		SCREAMING_SNAKE_CASE	:		Any				,		SCREAMING_SNAKE_CASE	:		Tuple     ):







      '''simple docstring'''




      lowerCAmelCase    		=			parameterized.to_safe_name("""_""".join(str(SCREAMING_SNAKE_CASE     ) for x in param.args     )     )
      return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
SCREAMING_SNAKE_CASE__   =  list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class 						lowercase   (   _UpperCAmelCase   ):





      @parameterized.expand(lowercase ,		name_func=lowercase					)
      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	int:
            self.run_and_check(
                stage=lowercase ,		model=lowercase ,		distributed=lowercase ,		fpaa=lowercase ,		)





      @require_torch_multi_gpu
      @parameterized.expand(lowercase ,		name_func=lowercase					)
      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	Tuple:
            self.run_and_check(
                stage=lowercase ,		model=lowercase ,		distributed=lowercase ,		fpaa=lowercase ,		)





      @parameterized.expand(lowercase ,		name_func=lowercase					)
      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	Optional[Any]:
            self.run_and_check(
                stage=lowercase ,		model=lowercase ,		distributed=lowercase ,		fpaa=lowercase ,		)





      @require_torch_multi_gpu
      @parameterized.expand(lowercase ,		name_func=lowercase					)
      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	int:
            self.run_and_check(
                stage=lowercase ,		model=lowercase ,		distributed=lowercase ,		fpaa=lowercase ,		)





      def        _snake_case     (			self ,		lowercase					)						->	str:
            # XXX: run_asr is premature and doesn't save any results
            # so all we check for now is that the process didn't fail
            pass





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase = 10 ,		lowercase = True ,		lowercase = True ,		lowercase = True ,		)						->	Union[str, Any]:
            lowerCAmelCase    		=			models[model]

            lowerCAmelCase    		=			self.run_trainer(
                stage=lowercase ,		model_name=lowercase ,		eval_steps=lowercase ,		num_train_epochs=1 ,		distributed=lowercase ,		fpaa=lowercase ,		)

            self.do_checks(lowercase					)

            return output_dir





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase = 10 ,		lowercase = 1 ,		lowercase = True ,		lowercase = True ,		)						->	Union[str, Any]:
            lowerCAmelCase    		=			self.get_auto_remove_tmp_dir("""./xxx""" ,		after=lowercase					)
            lowerCAmelCase    		=			f'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(lowercase					)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

            if fpaa:
                  args.extend(["""--fp16"""]					)

            # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
            # hence the separate config files
            lowerCAmelCase    		=			f'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
            lowerCAmelCase    		=			[f'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
            lowerCAmelCase    		=			self.get_launcher(lowercase					)

            lowerCAmelCase    		=			launcher + script + args + ds_args
            # keep for quick debug
            # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
            execute_subprocess_async(lowercase ,		env=self.get_env()					)

            return output_dir





      def        _snake_case     (			self ,		lowercase=False					)						->	Optional[int]:
            # 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
            # - it won't be able to handle that
            # 2. for now testing with just 2 gpus max (since some quality tests may give different
            # results with mode gpus because we use very little data)
            lowerCAmelCase    		=			min(2 ,		get_gpu_count()					) if distributed else 1
            return f'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()













"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'speech_to_text_2'
      _SCREAMING_SNAKE_CASE							      = ['past_key_values']
      _SCREAMING_SNAKE_CASE							      = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}





      def __init__(			self ,		lowercase=10_000 ,		lowercase=6 ,		lowercase=2_048 ,		lowercase=4 ,		lowercase=0.0 ,		lowercase=True ,		lowercase="relu" ,		lowercase=256 ,		lowercase=0.1 ,		lowercase=0.0 ,		lowercase=0.0 ,		lowercase=0.02 ,		lowercase=2 ,		lowercase=True ,		lowercase=1 ,		lowercase=0 ,		lowercase=2 ,		lowercase=1_024 ,		**lowercase ,		)						->	Optional[int]:
            lowerCAmelCase    		=			vocab_size
            lowerCAmelCase    		=			d_model
            lowerCAmelCase    		=			decoder_ffn_dim
            lowerCAmelCase    		=			decoder_layers
            lowerCAmelCase    		=			decoder_attention_heads
            lowerCAmelCase    		=			dropout
            lowerCAmelCase    		=			attention_dropout
            lowerCAmelCase    		=			activation_dropout
            lowerCAmelCase    		=			activation_function
            lowerCAmelCase    		=			init_std
            lowerCAmelCase    		=			decoder_layerdrop
            lowerCAmelCase    		=			use_cache
            lowerCAmelCase    		=			decoder_layers
            lowerCAmelCase    		=			scale_embedding  # scale factor will be sqrt(d_model) if True
            lowerCAmelCase    		=			max_target_positions

            super().__init__(
                pad_token_id=lowercase ,		bos_token_id=lowercase ,		eos_token_id=lowercase ,		decoder_start_token_id=lowercase ,		**lowercase ,		)






